Thermal energy from transcritical CO<inf>2</inf> heat pumps for small marine applications
Bibliographic record
Abstract
Heat pumps are a more efficient method to heat water compared to conventional electrical heating systems. However, the harmful environmental effects of traditional CFC and HCFC refrigerants require more sustainable alternatives to be adopted. In marine applications, leaked HCFCs can cause substantial harm to sensitive coastal ecology and are powerful greenhouse gases. Standard CO2(R744 when used as a refrigerant) is a promising replacement for HCFCs due to its low global warming and ozone depletion potential. This paper investigates adapting the traditional ground loop of a geothermal heat pump to a marine loop for use with seawater or coastal applications. New analytical results on the use of thermal energy from marine water for domestic water heating with a transcritical CO2heat pump are presented in this paper. Substantial research has recently been performed on transcritical CO2heat pump cycles, including cycle enhancements and optimization techniques. However, the research has been primarily focused on traditional heat pump configurations, with ambient air usually being the heat source. Geothermal temperatures vary on average between 8°C to 12°C throughout the year. A large body of water in this temperature range can provide a consistent source of thermal energy and thus effectively replace the ground source used in typical geothermal heat pumps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".